Artificial Intelligence · 24.08.2026, 07:16 UTC
S-AI-Recursive: Convergent Recursive Reasoning
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | arXiv cs.AI ↗ |
| Veröffentlicht | 24.08.2026 UTC |
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arXiv:2605.13872v2 Announce Type: replace-cross Abstract: This article introduces S-AI-Recursive, a bio-inspired Sparse Artificial Intelligence architecture in which reasoning is implemented as a hormonally regulated closed-loop iteration rather than a single feed-forward pass. The Recursive Reasoning Cycle (RRC) is governed by two recursive hormones: Clarifine, a convergence signal, and Confusionin, a residual-uncertainty signal. Their antagonistic interaction regulates state refinement, stopping, resource allocation, and recursive-engram retrieval. The revised framework distinguishes hormonal-subsystem stability from joint cognitive state-hormone convergence and gives explicit sufficient conditions for coupled contraction on fixed-point-structured tasks. It also includes Lyapunov analysis, conditional entropic contraction, multi-signal stopping, Euler-Maruyama discretization with projection, constrained agent selection, and warm-start memory. Experimental evaluation combines controlled SAI-UT+ simulations with exactly verifiable task-level tests. On convergent Maze instances, adaptive stopping reduces mean depth from 20.00 to 11.31 iterations at unchanged resolution, a 43.4 percent reduction. On compatible recurring Sudoku instances, warm-start reduces mean depth from 18.39 to 2.00 cycles, saving 16.39 cycles at unchanged resolution. ARC-style tasks are used to assess operator portability rather than full benchmark performance. Robustness tests show an advantage over residual-only stopping on deceptive plateaus, but not under homogeneous Gaussian noise. These results …
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